A IMPORTÂNCIA DA UTILIZAÇÃO DE DADOS DE ALTA RESOLUÇÃO TEMPORAL EM SISTEMAS DE MONITORAMENTO E ALERTA DE CHEIAS. ESTUDO DE CASO: ARROIO PERDIZES, CÂNION DO ITAIMBEZINHO (RS).

Código

END179

Autores

Rodrigo Borges da Fonseca Bins, Fernando Mainardi Fan, Maurício Andrades Paixão

Tema

4 - Sistemas de alerta operacionais

Resumo

The increasing insertion of human beings in environments close to water bodies, mainly related to the urban development in riverside areas and the completion of several other activities along riverbeds results in a great and constant exposure of these people to episodes of floods and floods resulting from rapid and/or extreme hydrological variations, as well as their consequences. Among the several existing prevention measures, flood forecasting and warning systems are among the most applied in the national and international scenario, due to their great ability to mitigate the possible impacts resulting from these events, be it material, social or human. In this context, the present work aimed to discuss the use of daily and subdial data in the development of an early warning system for the Boi River Trail, located in the Aparados da Serra National Park, in southern Brazil. The Trail, which receives a large amount of tourists during the year, has some historical records of emergencies related to isolation, rescue and even deaths of tourists and guides due to flood episodes. Telemetric hydro meteorological monitoring equipment was installed in places of interest in the Park, since the only data available was fluviometric levels of the Perdizes creek and precipitation, both only with daily temporal resolution. From the available series of these records, a discussion about the representability of the daily level and precipitation data in relation to the hydrological events and the stream responses to them versus the representability of the subdaily data obtained with the installed equipment was raised. The comparative analysis of daily and subdaily data confirmed the expected theory that the use of data with daily time interval is not sufficient (or adequate) for application in the on-site forecast models.